In this paper, an integrated dispatch planning model including carbon trading cost is designed with multiple types of constraints, backed by the power provider model and taking into account the uncertainty within the power system. Subsequently, relying on the Actor – Critic framework in reinforcement learning, the dynamic optimal dispatch model of energy in key industries is constructed, and the Deep Q-Network (DQN) algorithm is employed to fine – tune and train the model’s parameters, aiming to acquire the most optimal dispatch strategy. The outcomes of the simulation indicate that the model put forward in this paper can effectively exploit the carbon emission reduction potential of key industries within the framework of the dual – carbon goal, and there are also large differences in the power supply planning of key enterprises under different carbon trading costs. Therefore, By leveraging the intelligent physical data of the large – scale electric power model, it is possible to comprehensively explore the energy consumption of crucial industries within the power grid. so as to help the key industries to better formulate the dual-carbon target and decision-making, and to enhance the green development level of the key industries.
